Nonlinear Offset-Free Model Predictive Control based on Dynamic PLS Framework

被引：0

作者：

Zhao, Qiang ^{[1
]}

Jin, Xin ^{[1
]}

Yu, Huapeng ^{[1
]}

Lu, Shan ^{[2
]}

机构：

[1] Liaoning Petrochem Univ, Sch Informat & Control Engn, Fushun 113001, Peoples R China

[2] Shenzhen Polytech, Inst Intelligence Sci & Engn, Shenzhen 518055, Peoples R China

来源：

PROCESSES | 2021年 / 9卷 / 10期

基金：

中国国家自然科学基金;

关键词：

partial least square; model predictive control; nonlinear system; offset-free control; PARTIAL LEAST-SQUARES; EFFICIENCY;

D O I：

10.3390/pr9101784

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

A nonlinear offset-free model predictive control based on a dynamic partial least square (PLS) framework is proposed in this paper. A multi-output multi-input system is projected into latent variable space by a PLS outer model. For each latent variable model, the T-S fuzzy model is used to describe the nonlinear characteristics of the system; while the state-space model is used in T-S fuzzy model consequent parameters to describe the dynamic characteristics. A disturbance model is introduced in the state-space model. For model state variables, a state observer is used to compensate for the mismatch of the model. The case study results for the pH neutralization process show that the MPC controller based on this method can guarantee the tracking performance of the nonlinear system without static error.</p>

引用

页数：16

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